Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/394789
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dc.contributor.authorOsman Ahmed Abdalla.-
dc.contributor.authorM. Nordin Zakaria-
dc.contributor.authorSuziah Sulaiman-
dc.contributor.authorWan Fatimah Wan Ahmad-
dc.date.accessioned2023-06-15T07:50:18Z-
dc.date.available2023-06-15T07:50:18Z-
dc.identifier.otherukmvital:120673-
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/394789-
dc.description.abstractThis paper presents a Iced-forward Artificial Neural Network (ANN) model for prediction of isolate and normal pentene of debutanizer catalytic reforming unit. Temperature, reflux flow, anti flow rate are used as input variables to the network. isolate pentene (iC5), and normal pentene (nC5) are employed as the output variable. About 500 field data collected from PETRONAS Penapisan (Melaka) Sdn Bhd were used to develop the ANN model. The developed ANN model obtained by dividing the collected data set into three different group: training, validation, and testing group. Back-propagation algorithm was used to train the network. A correlation coefficient of 0.999 was obtained with standard deviation of 0.006 for iC5. For nC5 a 0.999 correlation coefficient and 0.005 standard deviation obtained.-
dc.language.isoeng-
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE),Piscataway, USA-
dc.subjectPredictions of isolate-
dc.subjectNormal pentene-
dc.subjectDebutanizer catalytic-
dc.subjectArtificial neural network-
dc.titlePredictions of isolate and normal pentene of debutanizer catalytic reforming unit by using artificial neural network-
dc.typeSeminar Papers-
dc.format.pages6-
dc.identifier.callnoT58.5.C634 2008 kat sem-
dc.contributor.conferencenameInternational Symposium on Information Technology 2008-
dc.coverage.conferencelocationKuala Lumpur Convention Centre, Malaysia-
dc.date.conferencedate26/08/2008-
Appears in Collections:Seminar Papers/ Proceedings / Kertas Kerja Seminar/ Prosiding

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